Geospatio-temporal library for notebooks
You can use the geospatio-temporal library to expand your data science analysis in Python notebooks to include location analytics by gathering, manipulating and displaying imagery, GPS, satellite photography and historical data.
The spatio-temporal library is available in all IBM Watson Studio Spark runtime environments and if you run your notebooks in IBM Analytics Engine.
The geospatial library includes functions to read and write data, topological functions, geohashing, indexing, ellipsoidal and routing functions.
Key aspects of the library include:
- All calculated geometries are accurate without the need for projections.
- The geospatial functions take advantage of the distributed processing capabilities provided by Spark.
- The library includes native geohashing support for geometries used in simple aggregations and in indexing, thereby improving storage retrieval considerably.
- The library supports extensions of Spark distributed joins.
- The library supports the SQL/MM extensions to Spark SQL.
Getting started with the library
Before you can start using the library in a notebook, you must register
STContext in your notebook to access the
from pyst import STContext stc = STContext(spark.sparkContext._gateway)
After you have registered
STContext in your notebook, you can begin exploring the spatio-temporal library for:
- Functions to read and write data
- Topological functions
- Geohashing functions
- Geospatial indexing functions
- Ellipsoidal functions
- Routing functions
Check out the following sample Python notebooks to learn how to use these different functions in Python notebooks: